FERNANDO GOMEZ-PERALTA, Spain

Hospital General de Segovia Endocrinology and Nutrition Unit

Presenter of 1 Presentation

ORAL PRESENTATION SESSION

EVALUATION OF THE HYPOGLYCAEMIA PREDICTIVE ALGORITHM IN THE INSULCLOCK® INSULIN PEN CAP DIGITAL PLATFORM IN TYPE 1 DIABETES TREATED WITH INSULIN MULTIDOSE.

Abstract

Background and Aims

Insulclock® is a small electronic device that functions as a cap fitted to the available insulin pens and monitors the date, time and dose of insulin, and information from glucometers and continuous glucose monitors (CGM). Store the information in an app designed for this purpose. Our goal was to evaluate the accuracy of an algorithm for hypoglycemia (HG) prediction by the Insulclock app using the device's information exclusively.

Methods

An original HG (glucose <70mg/dL) predictive algorithm was developed that uses data from Insulclock® and the Freestyle Libre® (Abbott) CGM. It alerts the risk of HG and the expected time up to it. Intakes are automatically detected using the GRID method. Subsequently, it has been evaluated for 180 days in a patient 47 years old with DM1 30 years ago. We consider correct alarms real HG avoided with an intake; false positive if after the expected time for HG, it does not arrive without having eaten; false negative, HG without previous alarm. Additionally, the error in the calculated time and the total number of HG events were evaluated.

Results

132 alarms issued. Correct alarms 90 (84.9%); false positives 42 (31.8%); false negatives 16 (15.1%); 116 actual HG events (77.5% detected). The average advance time detection was 87 minutes. The average absolute error value in the time prediction for hypoglycemia is 35 minutes.

Conclusions

The predictive algorithm tested allows to detect and alert in advance to potentially avoid a high number of hypoglycemia events using only information obtained automatically by the Insulclock®device. New studies should expand and confirm this experience.

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